本研究提出了使用GPU架構實作的二階段哼唱選歌系統。哼唱選歌 (Query by Singing and Humming, QBSH )是種利用人聲進行歌曲搜尋的方法，系統會採用使用者哼唱的片段並從資料庫中找出前十名最相似的歌曲。

為了增加比對速度，我們先使用了線性伸縮並從擁有八千四百三十一首流行歌的資料庫中找出較為可能的候選歌曲，接著會對這些候選歌進行動態時間校正比對以求得較好的效能。經過了最佳化微調以及合併方法的改進後，該系統能夠比純粹在GPU上使用動態時間校正快上7倍，且辨識率能達到77.65%。

關鍵字：音樂檢索、哼唱選歌、線性伸縮、動態時間校正、GPU

This research proposes the use of GPU (graphic processing unit) to implementing a two-stage comparison method for a QBSH (query by singing/humming) system. The system can take a user’s singing or humming and retrieve the top-10 most likely candidates from a database of 8431 songs.

In order to speed up the comparison, we apply linear scaling in the first stage to select candidate songs from the database. These candidate songs are then re-ranked by dynamic time warping to achieve better recognition accuracy in the second stage. With the optimum setting and improvement of combination method, we can achieve a speedup factor of 7 (compared to dynamic time warping on GPU) and an accuracy of 77.65%.

Keyword: Music Retrieval, Query by Singing and Humming, Linear Scaling, Dynamic Time Warping, GPU

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